System and method for forecasting production from a hydrocarbon reservoir

ABSTRACT

A system and method is taught to substantially automate forecasting for a hydrocarbon producing reservoir through integration of modeling module workflows. A control management module automatically generates static and dynamic offspring models, with static and dynamic modeling software, until a performance objective associated with the forecasting of the reservoir is satisfied. The performance objective can include an experimental design table to determine a sensitivity of a particular parameter or can be directed towards reservoir optimization, i.e., ultimate hydrocarbon recovery, net present value, reservoir percentage yield, reservoir fluid flow rate, or history matching error.

RELATED APPLICATIONS

The present application is a continuation-in-part of and claims thebenefit of patent application U.S. Ser. No. 11/848,348, filed Aug. 31,2007 now U.S. Pat. No. 8,335,677, which claims benefit of provisionalpatent application U.S. Ser. No. 60/841,858, filed Sep. 1, 2006, whichthe entirety of each application are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention is generally related to forecasting hydrocarbonproduction from a subterranean reservoir, and more particularly, tointegrated workflows for static and dynamic reservoir modeling forforecasting hydrocarbon production from a subterranean reservoir.

BACKGROUND OF THE INVENTION

Decisions to develop new reservoir fields and how to efficiently manageproduction of current fields are of great significance in the petroleumindustry. These decisions must be knowledgeable and often made in atimely manner. In situations where capital expenses are high and otherrisks abundant, carefully analyzing inherent uncertainties of thereservoir field and how they impact the forecasted production becomes adaunting task. This is especially true when coupled with stringentdeadlines, or when in challenging environments such as for offshorereservoirs. If business decisions are made based on incomplete or poorcharacterizations of the reservoir, or if the characterizations of thereservoir are delayed, substantial financial loss can occur due topremature hydrocarbon contract negotiations, incomplete or inaccuratereservoir certifications, poorly negotiated service contracts,non-optimized recovery of the hydrocarbon reserves, or a combinationthereof.

Accurately forecasting the performance of a reservoir requires realisticstatic and dynamic models representative of the reservoir. Static anddynamic models can be generated from a plurality of different workflows,and are generally populated with the available engineering andgeological data of the reservoir. Depending on the complexity andlocation of the reservoir, this data can be limited as costs can becomeprohibitive.

Static models generally comprise a structural and stratigraphicframework populated with parameters such as sedimentological properties,permeability distributions, porosity distributions, fluid contacts, andfluid saturations. There are many commercially available products forconstructing static models, such as Earth Decision Suite (powered byGOCAD™) distributed by Paradigm Geotechnology BV headquartered inAmsterdam, The Netherlands and Petrel™ from Schlumberger Limitedheadquartered in Houston, Tex. Static models are typically constructedby a team including geologists, geophysicists, and stratigraphers usingreservoir data from a variety of sources such as core samples, welllogs, and seismic surveys. Depending on the data interpretation andchosen modeling package, multiple realizations of the same geology maybe made, leading to similar, yet different geological models beingshaped by quasi-random variations. A certain amount of validation thatthe static model is an appropriate geological interpretation can begiven with additional geochemical and geostatistical analysis; however,only a certain amount of deterministic information may be extracted fromthe subterranean formation, and one typically relies on applyingprobabilistic methods in combination with the obtained data to constructa reasonable static reservoir model.

Dynamic models typically comprise upscaled versions of static modelspopulated with additional information such as reservoir fluid flow ratesand reservoir pressure-volume-temperature (PVT) characteristics. Theupscaling process entails coarsening the fine-scale resolution of thestatic model to allow for computational tractability. There are alsomany commercially available products for building these dynamic models,such as Chevron's proprietary CHEARS™ simulation-package orSchiumberger's ECLIPSE™ reservoir simulator. Dynamic models are normallyconstructed by reservoir engineers, for which a different skill profileis typically demanded in comparison to the assemblers of the staticmodel.

Due to the multi-disciplinary roles needed to construct static anddynamic models of a subterranean reservoir, certain significantintricacies of the reservoir's geology can be occasionally surrenderedas the importance of these aspects are overlooked or simply undervalued.

Once production of the reservoir begins, it is desired that the staticand dynamic models be continuously updated with new production data sothat they may reliably predict future extraction amounts. This isanother challenge, as the real time data should be managed and filteredto best use it effectively to delineate the reservoir model descriptionand flow parameters. Determining what production data is useful and thenmanually inputting this data into the model is often tedious and verytime consuming. Typically, the static model needs to first beconditioned with the filtered production data, and reconstructedcreating a static offspring model. The static parameters of this newoffspring model are then applied to the dynamic model so that it can besimulated, thus producing an updated dynamic model. This process isiteratively repeated, typically in a linear fashion, to eventuallygenerate an updated reservoir production forecast. One becomes limitedin the number of model iterations they can run due to the time and costconstraints needed to perform a proper optimization investigation. Theperformance predictions may also not be “optimized” due to poor analysisof the many inherent uncertainties of the static and dynamic models.Additionally, loss of data or its significance may be overlooked duringtransfer of data between the reservoir production engineers, the staticmodeling team, and the dynamic modeling team.

The updated reservoir forecast is used to sufficiently understand thecomplex chemical, physical and fluid flow processes occurring in thereservoir to predict future reservoir behavior and maximize the ultimatehydrocarbon recovery. To test the reliability of the forecast, historymatching techniques can be employed. These techniques attempt to findplausible flow solutions by trying to mimic past performances throughsimulation of the reservoir model and comparing the outcome with actualproduction data. Mathematically, this is done by searching for minima ofan objective function below a predetermined threshold value. In certaincircumstances, a good match may not be found and one must rely on thebest apparent match produced. In other instances, a plurality ofacceptable history matching solutions may be found leading toinconclusive predictions.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, a computer implementedmethod is disclosed to automate forecasting of a hydrocarbon producingreservoir. The method includes providing a performance objectiveassociated with forecasting of a hydrocarbon producing reservoir. Theperformance objective is provided via a user interface that is incommunication with a reservoir modeling program. A static modelrepresentative of at least a portion of the hydrocarbon producingreservoir is generated with a static modeling module. A dynamic modelrepresentative of at least a portion of the hydrocarbon producingreservoir is generated with a dynamic modeling module. A controlmanagement module iteratively combines the static and dynamic models togenerate at least one offspring model. The at least one offspring modelis generated with at least one of the static and dynamic modelingmodules. Generation of the at least one offspring model continues untilthe performance objective is satisfied. Once the performance objectiveis satisfied, a result according to the performance objective, which isassociated with the forecasting of the hydrocarbon producing reservoir,is output.

Another aspect of the present invention includes a computer implementedmethod to automate forecasting of a hydrocarbon producing reservoir. Themethod includes modeling a static model representative of at least aportion of a hydrocarbon producing reservoir with a static modelingmodule. The method includes modeling a dynamic model representative ofat least a portion of the hydrocarbon producing reservoir with a dynamicmodeling module. A control management module generates a discrete set ofparameters responsive to the static model and the dynamic model. Anoffspring model is generated with at least one of the static and dynamicmodeling modules The offspring model is generated responsive to at leastone parameter from the discrete set of parameters. The discrete set ofparameters is updated responsive to the offspring model. The offspringmodel is iteratively refined until it has been generated a predeterminednumber of iterations, the offspring models converge to a predeterminedvariance, or the offspring models have been generated a sufficientnumber of iterations to determine a sensitivity of the parameter. Avisual display of a solution associated with the forecasting of thehydrocarbon producing reservoir is output.

Another aspect of the present invention includes a system to automateforecasting of a hydrocarbon producing reservoir. The system includes acomputer processor and a computer program executable on the computerprocessor. The computer program includes a first modeling module toprovide a first model representative of at least a portion of thehydrocarbon producing reservoir. The computer program includes a secondmodeling module to provide a second model representative of at least aportion of the hydrocarbon producing reservoir. The computer programincludes a control management module that generates offspring modelsresponsive to at least one of the first model and the second model. Theoffspring models are generated with at least one of the first modelingmodule and the second modeling module. Generation of the offspringmodels continues until a performance objective associated with theforecasting of the system is satisfied. The control management moduleoutputs a result according to the performance objective subsequent tothe performance objective being satisfied.

Another aspect of the present invention includes a system to automateforecasting of a hydrocarbon producing reservoir. The system includes acomputer processor and a software program executable on the computerprocessor. The software program includes a first modeling module toproduce a first model representative of at least a portion of thehydrocarbon producing reservoir. The software program includes a secondmodeling module to produce a second model representative of at least aportion of the hydrocarbon producing reservoir. The software programincludes a control management module capable of initiating execution ofthe software program responsive to a performance objective. The controlmanagement module includes a model retriever to retrieve the first andsecond models over a communications network. The control managementmodule includes a model generator to generate offspring modelsresponsive to the first and second models with the first and secondmodeling modules. The control management module includes a proxygenerator to generate a proxy function responsive to the first andsecond models. The control management module includes a model filtercapable of prohibiting the model generator from generating offspringmodels responsive to the proxy function. The control management moduleincludes a model evaluator to evaluate the offspring models and generatea forecast for the hydrocarbon producing reservoir. The system includesa user control interface for inputting information, including aperformance objective, into the system. The system includes a databaseto store the performance objective, first and second models, andoffspring models. The system includes a user reporting means incommunication with the control management software over thecommunications network to output the forecast for the hydrocarbonproducing reservoir.

Another aspect of the present invention includes software stored on aprocessor readable medium to substantially automate forecasting for ahydrocarbon producing reservoir. The software includes a model retrieverto retrieve a model representative of at least a portion of thehydrocarbon producing reservoir from a modeling, module. The softwareincludes a proxy generator to generate a proxy function responsive tothe model. The software includes a model generator to generate anoffspring model with the modeling module that is responsive to themodel. The software includes a model filter capable of prohibiting themodel generator from generating the offspring model responsive to theproxy function. The software includes a model evaluator to evaluate theoffspring model and generate a forecast for the hydrocarbon producingreservoir.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view illustrating a physical geologic volume of asubterranean reservoir containing hydrocarbons, in accordance with thepresent invention.

FIG. 2 is a flowchart illustrating steps of an integrated modelingworkflow, in accordance with the present invention.

FIG. 3 is a schematic flow diagram illustrating steps used in a systemhaving a first modeling module, second modeling module, and controlmanagement module, in accordance with the present invention.

FIG. 4 is a schematic flow diagram illustrating steps used in a systemhaving a static modeling module, dynamic modeling module, and controlmanagement module, in accordance with the present invention.

FIG. 5 is a schematic flow diagram illustrating a control managementmodule utilizing proxies, in accordance with the present invention.

FIG. 6 is a schematic diagram of a system for forecasting hydrocarbonproduction of a reservoir, in accordance with the present invention.

FIG. 7 is a schematic diagram of a system for forecasting hydrocarbonproduction of a reservoir, in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention described herein provide forefficient systems and methods that integrate workflows of computationalmodeling. Such systems and methods are generally applied to forecastingof hydrocarbon production from a subterranean reservoir.

FIG. 1 depicts a physical geologic volume of a subterranean reservoir 1.Subterranean reservoir 1 is comprised of an upper surface 3, which canextend to the earth's physical surface, and a plurality of strata 5located beneath upper surface 3. The plurality of strata 5 are typicallycomposed of parallel layers of rock and fluid material eachcharacterized by different sedimentological and fluid properties.Hydrocarbons 9 may accumulate below or between non-porous or lowerpermeability rock formations 7. If a large enough pool of hydrocarbons 9is discovered, extraction of hydrocarbons 9 from subterranean reservoir1 is typically desired. Practice of the present invention is generallyapplicable to the forecasting of hydrocarbon production from reservoirs,such as subterranean reservoir 1.

FIG. 2 is a flowchart illustrating steps of an integrated modelingworkflow to method 10 to substantially automate forecasting for ahydrocarbon producing reservoir. In step 11, method 10 includes modelinga first model representing at least a portion of a hydrocarbon producingreservoir, such as subterranean reservoir 1. As will be described morefully herein, a first modeling module is capable of generating the firstmodel. In step 13, method 10 includes modeling a second modelrepresenting at least a portion of a hydrocarbon producing reservoir,such as subterranean reservoir 1. As will be described more fullyherein, a second modeling module is capable of generating the secondmodel. In step 15, a performance objective is provided. The performanceobjective drives the desired forecasting outcome and typically is eitheran optimization study or uncertainty assessment. In some embodiments,the performance objective is manually selected by an operator via a userinterface. As shown in step 17, an offspring model is generated witheither of the first or second modeling modules. The offspring model isgenerated once the performance objective has been determined in step 15and the first and second models have been constructed in steps 11 and 13respectively. The offspring model is a progeny of the first and secondmodel, as it is generated from a variety of parameters from each model.In step 19, it is determined whether or not the performance objective ofstep 15 has been satisfied. As will be described in more detail herein,if the generation of the newly constructed offspring model does notsatisfy the performance objective, another offspring model isconstructed and step 17 is repeated. This iterative process continuesuntil the performance objective is satisfied. Once the performanceobjective is satisfied, a result associated with the forecasting for ahydrocarbon producing reservoir can be produced, as shown in step 21.The result can be output from the system as a visual displayrepresenting a solution associated with the hydrocarbon producingreservoir forecast.

As an example, if the performance objective of step 15 is anoptimization study, the result can include solving an objective functionassociated with the forecasting for a hydrocarbon producing reservoir.The objective function can maximize or minimize ultimate hydrocarbonrecovery, net present value (NPV), reservoir percentage yield, reservoirfluid flow rate, history matching error, or a combination thereof.Iterations related to determining whether the performance objective issatisfied can continue in step 19 until the offspring model is generateda predetermined number of iterations or the offspring model converges toa predetermined variance. Decision parameters that represent thesolution to the objective function can be output once the performanceobjective is satisfied. These decision parameters can be output from thesystem as a graphical display or in tabular format. Examples of such anoutput include, but are not limited to, a visual display of cumulativedistribution functions, a visual display of multiple simulation resultsgenerated from a combination of decision parameters, a visual display ofsimulated parameters in time, or a combination thereof.

By way of another example, if the performance objective of step 15 is anuncertainty assessment, the forecast can be a result to an experimentaldesign table. The result can be a sensitivity of one or more ofpermeability distributions porosity distributions, fluid contacts, fluidsaturations, geobody connectivity, pore volume, fault transmissibility,sedimentological properties, reservoir fluid flow rates, reservoirpressure characteristics, reservoir temperature characteristics,upscaling properties, and training images. In this case, the performanceobjective can be considered satisfied once the offspring models havebeen generated a sufficient number of iterations to determine thesensitivity of the one or more parameters in the design table. Thesesensitivities can be output from the system once the performanceobjective is satisfied. These sensitivities can be output as a graphicaldisplay or in tabular format. For example, the output can be a visualdisplay of multiple simulation results generated from a combination ofsensitivities.

FIG. 3 is a flowchart illustrating system 20 having an integratedmodeling work-flow between a first modeling module 30, a second modelingmodule 40, and a control management module 50. First modeling module 30and second modeling module 40 are used to generate models representingat least a portion of a hydrocarbon producing reservoir, such assubterranean reservoir 1. Control management module 50 is capable ofcontrolling a plurality of operations for system 20.

In first modeling module 30, reservoir data 32 and defined parameters 34can be used to obtain a discrete set of parameters 36 the discrete setof parameters 36 can be used to generate a model 38 that isrepresentative of at least a portion of the hydrocarbon producingreservoir. As will be described more fully herein, first modeling module30 can be a static or dynamic modeling module.

In second modeling module 40, reservoir data 42 and defined parameters44 can be used to obtain a discrete set of parameters 46. The discreteset of parameters 46 can be used to generate a model 48 that isrepresentative of at least a portion of the hydrocarbon producingreservoir. As will be described more fully herein, second modelingmodule 40 can be a static or dynamic modeling module.

A plurality of operations for system 20 are controlled by controlmanagement module 50. A performance objective 52 is determined anddrives the desired forecasting outcome that is associated with thehydrocarbon production of the subterranean reservoir. Based on theperformance objective 52, which is typically an optimization study oruncertainty assessment, the control management module 50 selectsdiscrete sets of parameters 54. The discrete sets of parameters 54 areselected from stored parameters pertaining to models 38, 48 and can beused for generating offspring models until the performance objective 52has been satisfied. Once the performance objective 52 has beensatisfied, a result 56, in accordance with performance objective 52 andassociated with the forecasting of hydrocarbon production from thesubterranean reservoir, can be produced.

In operation of system 20, reservoir data 32 is loaded into firstmodeling module 30 and reservoir data 42 is loaded into second modelingmodule 40, respectively in steps 31 and 41. In each modeling module,uncertain parameters 34 and 44 are then defined respectively in steps 33and 43. In certain embodiments, the user defines uncertain parameters 34and 44. Uncertain parameters for first modeling module 30 and secondmodeling module 40 can include permeability distributions, porositydistributions, fluid contacts, fluid saturations, geobody connectivity,pore volume, fault transmissibility, sedimentological properties,reservoir fluid flow rates, reservoir pressure characteristics,reservoir temperature characteristics, upscaling properties, andtraining images. Reservoir data 32 and defined parameters 34 are thenused in step 35 of first modeling module 30 to obtain a discrete set ofparameters 36, which can then be used for generating model 38 in step37. Similarly, in second modeling module 40, reservoir data 42 anddefined parameters 44 are used in step 45 to obtain a discrete set ofparameters 46, which can then be used for generating model 48 in step47.

The workflow of control management module 50 begins with determining aperformance objective 52, as shown in step 51. During operation ofsystem 20, the performance objective 52 drives the desired forecastingoutcome and typically is either an optimization study or uncertaintyassessment. The performance objective can be manually selected by anoperator and in other instances the performance objective can bepredefined by system 20. Once the performance objective 52 has beendetermined, control management module 50 retrieves and stores parameterspertaining to model 38 from the first modeling module 30 and parameterspertaining to model 48 from the second modeling module 40 in step 53. Instep 55, a discrete set of parameters 54 is selected from the parameterspreviously stored in step 53. If the performance objective 52 has notbeen satisfied at step 57, the control management module 50 initiatesmodeling of the discrete set of parameters 54 in step 59. If the controlmanagement module 50 initiates modeling with the first modeling module30, the discrete set of parameters 54 can be used as the discrete set ofparameters 36 in step 35 for generation of an offspring model 38.Similarly, if the control management module 50 initiates modeling withthe second modeling module 40, the discrete set of parameters 54 can beused as the discrete set of parameters 46 in step 45 for generation ofan offspring model 48. The control management module 50 then updates orreplaces the previously stored parameter sets in step 53 with theparameters characterizing the newly generated offspring models 38, 48.It will be appreciated by one skilled in the art that both firstmodeling module 30 and second modeling module 40 can be operatedconcurrently. Refinement of the parameter set continues until theperformance objective 52 has been satisfied in step 57, at which point aresult 56 associated with forecasting for the hydrocarbon producingreservoir can be produced in step 61.

FIG. 4 illustrates system 60, which is a variation of system 20 shownin. FIG. 3, where first modeling module. 30 is now represented by staticmodeling module 70, second modeling module 40 is now represented bydynamic modeling module 80, and control management module 50 in nowrepresented by control management module 90.

Static modeling module 70 performs static modeling of a reservoir usingreservoir data 72. The static model incorporates seismic data to build arough structural and stratigraphic framework. This framework is thenpopulated with parameters such as sedimentological properties,permeability and porosity distributions, fluid contacts, and fluidsaturations. A plurality of model outcomes may be calculated based onthe populated framework, as each assigned parameter or parameter rangeattributes a certain amount of indefiniteness or inherent uncertainty.Items such as poor seismic images or areas below the threshold ofseismic resolution may increase this uncertainty. For instance, it maybe difficult to distinguish whether a particular geobody is comprised ofsand or shale from acoustic impedance alone and a best guess range maybe inputted into the static model. These static uncertain parameters 74can be defined and used with reservoir data 72 to obtain a discrete setof static parameters 76, which can be used for generating static model78. One skilled in the art will appreciate that the workflow of staticmodeling module 70 can be scriptable to allow for automatic generationof static models. In this scenario, the individual steps of generating astatic model are manually performed and recorded into a script.Uncertain static parameters 74 can then be input into the script and thestatic modeling module 70 is capable of automatically generating astatic model.

Dynamic modeling module 80 begins construction of a dynamic model of areservoir by upscaling a static model, such as static model 78, as thefine scale properties of a static model tend to exceed currentcomputational capabilities when paired with flow simulation. A range oflocal and global techniques have been developed to ensure that theupscaled approximations serve as a robust interpretation of the staticgeological model complexities. Spatial resolution is applied to thecoarser model, such that the model is layered with thousands or evenmillions of individual cells defining a grid. Accordingly, the model canbe analyzed using various algorithms to determine potential interactionsor connectivity between the individual cells. Certain grids may beunstructured or structured depending on whether the model should beconstrained to preserve certain stratigraphic or sedimentologicalcharacteristics. The upscaled model is populated with uncertain staticand dynamic parameter data such as sedimentological properties,permeability and porosity distributions, fluid contacts, fluidsaturations, reservoir fluid flow rates and PVT characteristics. Similarto the parameters used in static modeling, each assigned parameter orparameter range attributes a certain amount of indefiniteness oruncertainty. Once these static and dynamic uncertain parameters 84 aredefined, they can be used with static and dynamic reservoir data 82 toobtain a discrete set of static and dynamic parameters 86, which canthen be used for generating dynamic model 88.

A plurality of operations for system 60 are controlled by controlmanagement module 90. Similar to system 20, a performance objective 92is determined and drives the desired forecasting outcome. Based on theperformance objective 92, the control management module 90 selectsdiscrete sets of static and dynamic parameters 94. The discrete sets ofstatic and dynamic parameters 94 are selected from stored parameterspertaining to models 78, 88 and can be used for generating static anddynamic offspring models until the performance objective 92 has beensatisfied. Once the performance objective 92 has been satisfied, aresult 96 associated with the forecasting of hydrocarbon production fromthe subterranean reservoir, which is in accordance with the performanceobjective 92, can be produced.

In operation of system 60, static reservoir data 72 is loaded intostatic modeling module 70 and static and dynamic reservoir data 82 isloaded into dynamic modeling module 80, respectively in steps 71 and 81.In each modeling module, uncertain parameters 74 and 84 are then definedrespectively in steps 73 and 83. In certain embodiments, the userdefines uncertain parameters 74 and 84. In other embodiments, system 60call attribute predefined ranges to uncertain parameters 74 and 84.Uncertain parameters for static modeling module 70 can includepermeability distribution, porosity distributions, fluid contacts, fluidsaturations, geobody connectivity, pore volume, fault transmissibility,and sedimentological properties. In addition to these static uncertainparameters, dynamic modeling module 80 can also include dynamicuncertain parameters including uncertainty in reservoir fluid flowrates, reservoir pressure characteristics, reservoir temperaturecharacteristics, upscaling properties and training images. Staticreservoir data 72 and defined uncertain parameters 74 are used in step75 of static modeling module 70 to obtain a discrete set of staticparameters 76, which can then be used for generating static model 78 instep 77. Similarly, in dynamic modeling module 80, static and dynamicreservoir data 82 and defined uncertain static and dynamic parameters 84are used in step 85 to obtain a discrete set of static and dynamicparameters 86, which can then be used for generating dynamic model 88 instep 87.

The workflow of control management module 90 begins with determining aperformance objective 92, as shown in step 91. During operation ofsystem 60, the performance objective 92 drives the desired forecastingoutcome and typically is either an optimization study or uncertaintyassessment. The performance objective 92 can be manually selected by anoperator or the performance objective 92 can be predefined by system 60.Once the performance objective 92 has been determined, controlmanagement module 90 retrieves and stores parameters pertaining tostatic model 78 from static modeling module 70 and parameters pertainingto dynamic model 88 from dynamic modeling module 80, as shown in step93. In step 95, a discrete set of static and dynamic parameters 94 isselected from the parameters previously stored in step 93. If theperformance objective 92 has not been satisfied at step 97, the controlmanagement module 90 initiates modeling of the discrete set of staticand dynamic parameters 94 in step 99. In step 101, it is determinedwhether static modeling for the discrete set of static and dynamicparameters 94 has been completed. If static modeling has not beencompleted, the control management module 90 initiates modeling with thestatic modeling module 70 such that the static parameters from thediscrete set of static and dynamic parameters 94 can be used as thediscrete set of static parameters 76 in step 75 for generation of astatic offspring model 78. If static modeling has been completed, thecontrol management module 90 initiates modeling with the dynamicmodeling module 80. In this scenario, the discrete set of static anddynamic parameters 94 can be used as the discrete set of static anddynamic parameters 86 in step 85 for generation of a dynamic offspringmodel 88. Once a static or dynamic offspring model has been generated,the control management module 90 updates or replaces the previouslystored parameter sets in step 93 with the parameters characterizing thenewly generated static or dynamic offspring models, 78 or 88,respectively. It will be appreciated by one skilled in the art, thatboth static modeling module 70 and dynamic modeling module 80 can beoperated concurrently. Iterative refinement of the parameter set storedin step 93 continues until the performance objective 92 is satisfied instep 97. Once the performance objective 92 is satisfied, a result 96associated with forecasting for the hydrocarbon producing reservoir isproduced in step 103.

As shown in FIG. 5, control management module 110 can alternatively beused in system 60 in place of control management module 90. Similar tocontrol management module 90, a performance objective 112 is determinedthat drives the desired forecasting outcome. Based on the performanceobjective 112 and the parameters pertaining to models 78 and 88, controlmanagement module 110 can generate one or more proxy functions 114.

Control management module 10 can utilize proxy functions 114 to filterthe selection of offspring models. Proxy functions 114 can also beutilized as an inexpensive and reliable surrogate to numericalsimulation. Depending on the performance objective 112, the quantity andtype of proxy functions 114 created can vary. For example, if theperformance objective is an uncertainty assessment, a Kriging proxy, aneural network proxy, a splines proxy, and/or a polynomial proxy may beutilized. One skilled in the art will appreciate that numerous proxiescan theoretically be created, assuming enough data is available, as onecan always increase to a higher order polynomial proxy; however,typically applying up to a second order polynomial with cross-terms(i.e., a linear polynomial, a quadratic polynomial, and a quadraticpolynomial with cross terms) is sufficient. If the performance objectiveis an optimization study, a Kriging proxy, a neural network proxy,and/or a splines proxy can be used.

After formulation of proxy functions 114, control management module 110selects discrete sets of static and dynamic parameters 116. The discretesets of static and dynamic parameters 116 can be used for iterativelygenerating static and dynamic offspring models until the performanceobjective 112 is satisfied. A result 118 associated with the forecastingof hydrocarbon production from the subterranean reservoir, which is inaccordance with the performance objective 112, is then produced.

In operation, the workflow of control management module 110 begins withdetermining a performance objective 112, as shown in step 111. Duringoperation of system 60, the performance objective 112 drives the desiredforecasting outcome and typically is either an optimization study oruncertainty assessment. The performance objective 112 can be manuallyselected by an operator and in other instances the performance objective112 can he predefined by system 60. Once the performance objective 112has been determined, control management module 110 retrieves and storesparameters pertaining to static model 78 from static modeling module 70and parameters pertaining to dynamic model 88 from dynamic modelingmodule 80 in step 113. In step 115, one or more proxy functions 114 aregenerated based on the performance objective 112 and parameterspertaining to models 78 and 88. A discrete set of static and dynamicparameters 116 is selected in step 117 from the static and dynamicparameters previously stored in step 113.

If the performance objective 112 has not been satisfied at step 119, thecontrol management module 110 determines in step 121 whether one or moreproxy functions 114 have already been formulated. If proxy functions 114do exist, it is determined in step 123 whether the estimation providedby the one or more proxy functions 114 is reliable for the likelyoutcome of modeling the discrete set of static and dynamic parameters116. If the proxy estimation is reliable, a new discrete set of staticand dynamic parameters 116 is selected in step 117. If proxy functions114 do not exist in step 121 or if the proxy estimation is not reliablein step 123, the control management module 110 initiates modeling of thediscrete set of static and dynamic parameters 116 in step 125.

In step 127, it is determined whether static modeling for the discreteset of static and dynamic parameters 116 has been completed. If staticmodeling has not been completed, the control management module 110initiates modeling with the static modeling module 70 such that thestatic parameters from the discrete set of static and dynamic parameters116 can be used as the discrete set of static parameters 76 in step 75for generation of a static offspring model 78. If static modeling hasbeen completed, the control management module 110 initiates modelingwith the dynamic modeling module 80. In this scenario, the discrete setof static and dynamic parameters 116 can be used as the discrete set ofstatic and dynamic parameters 86 in step 85 for generation of a dynamicoffspring model 88.

Once a static or dynamic offspring model has been generated, the controlmanagement module 110 updates or replaces the previously storedparameter sets in step 113 with the parameters characterizing the newlygenerated static or dynamic offspring models, 78 or 88, respectively. Ifthe new set of parameters characterizing the newly generated static ordynamic offspring models form a new generation of model simulations, newproxy functions 114 can be generated. A new generation of modelsimulations is determined by the use of genetic algorithms, which arewell known by those skilled in the art. Genetic algorithms sample theparameter set and determine the average population fitness, or in otherwords, the arithmetic average of the objective function retrieved aftereach simulation run. If the fitness level between simulations runsimproves by a predetermined threshold, a new generation of simulationsis considered to occur.

Iterative refinement of the discrete set of static and dynamicparameters 116 stored in step 113 continues until the performanceobjective 112 is satisfied in step 119. Once the performance objective112 is satisfied, a result 118 associated with forecasting for thehydrocarbon producing reservoir is produced in step 129.

FIG. 6 illustrates a system 200 by which hydrocarbon productionforecasts for a reservoir are made according to an embodiment of thepresent invention. System. 200 includes user interface 201, such that anoperator can actively input information and review operations of system200. User interface 201 can be any means in which a person is capable ofinteracting with system 200 such as a keyboard, mouse, or touch-screendisplay. Input that is entered into system 200 through user interface201 can be stored in a database 203. Additionally, any informationgenerated by system 200 can also be stored in database 203. For example,database 203 can store user-defined parameters, as well as, systemgenerated offspring models. Accordingly, models and parameters 205,performance objectives 207, and proxy functions 209 are all examples ofinformation that can be stored in database 203.

System 200 includes software 211 that is stored on a processor readablemedium. Current examples of a processor readable medium include, but arenot limited to, an electronic circuit, a semiconductor memory device, aROM, a flash memory, an erasable programmable ROM (EPROM), a floppydiskette, a compact disk (CD-ROM), an optical disk, a hard disk, and afiber optic medium. Software 211 includes first modeling software 213capable of generating a model representative of a reservoir, such assubterranean reservoir 1. Software 211 includes second modeling software215 capable of generating a model representative of a reservoir, such assubterranean reservoir 1. First and second modeling software, 213 and215 respectively, can be either static or dynamic modeling software.Software 211 also includes control software 217. As will be describedmore fully herein, control software 217 includes a plurality of modulesincluding a model retriever 219, a model generator 221, a proxygenerator 223, a model filter 225, and a model evaluator 227. Processor229 interprets instructions to execute software 211, as well as,generates automatic instructions to execute software for system 200responsive to predetermined conditions. Instructions from both userinterface 201 and control software 217 are processed by processor 229for operation of system 200. In some embodiments, a plurality ofprocessors can be utilized such that system operations can be executedmore rapidly.

In certain embodiments, system 200 can include reporting unit 231 toprovide information to the operator or to other systems (not shown). Forexample, reporting unit 231 can be a printer, display screen, or a datastorage device. However, it should be understood that system 200 neednot include reporting unit 231, and alternatively user interface 201 canbe utilized for reporting information of system 200 to the operator.

Communication between any components of system 200, such as userinterface 201, database 203, software 211, processor 229 and reportingunit 231, can be transferred over a communications network 233.Communications network 233 can be any means that allows for informationtransfer. Examples of such a communications network 233 presentlyinclude, but are not limited to, a switch within a computer, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), and a global area network (GAN). Communications network 233 canalso include any hardware technology used to connect the individualdevices in the network, such as an optical cable or wireless radiofrequency.

In operation, an operator inputs data, such as undefined parameters,through user interface 201 into database 203 and then initiates firstand second modeling software, 213 and 215, respectively, to generatemodels representative of a subterranean reservoir. Constructed models,comprised of a plurality of parameters embodying characteristics of thereservoir, are stored as models and parameters 205 in database 203. Oncethe performance objective 207 has been defined for system 200, either bythe operator through user interface 201 or by the control software 217,the control software 217 can initiate iterative refinement of thereservoir models using first modeling software 213 and second modelingsoftware 215.

Iterative refinement of the reservoir models begins with the modelretriever 219 of control software 217 retrieving models and parameters205 stored in the database 203. Model generator 221 can generatenumerous offspring models responsive to models and parameters 205. Modelgenerator 221 controls the type of software used to generate eachoffspring model, i.e., first modeling software 213 and second modelingsoftware 215. As models are generated, proxy generator 223 can generateproxy functions responsive to the models. Typical types of proxyfunctions that are generated by proxy generator 223 include krigingproxies, neural network proxies, splines proxies, and polynomialproxies. Formulated proxy functions 209 are stored in database 203 foreach generation of simulated models. Proxy functions 209 can be used toovercome the computational infeasibility of simulating detailedgeological models. For instance, one can tune proxy functions 209through uncertainty assessments and utilize them as inexpensive andreliable surrogates to forecast the production from a reservoir. Inother instances, such as performing an optimization study, proxyfunctions 209 can be used by model filter 225 to prohibit modelgenerator 221 from generating particular offspring models. Offspringmodels that are generated by first modeling software 213 or secondmodeling software 215 are evaluated by model evaluator 227 to determinewhether the offspring model satisfies the stored performance objective207. If the offspring model does not satisfy the performance objective207, iterative refinement of the reservoir models continues. If theoffspring model satisfies the performance objective 207, a forecast canbe generated for the hydrocarbon producing reservoir. The forecast canbe output to the operator with reporting unit 231 or can be providedthrough user interface 201.

FIG. 7 illustrates process or system 300, similar to process or system200 shown in FIG. 6, where the system architecture includes threedistinct modules.

First modeling module 301 is used to generate models representative of areservoir, such as subterranean reservoir 1. First modeling module 301includes a user interface 303 in which the operator can enter data,review ongoing operations, or control operation of first modeling module301. User interface 303 can be any means in which a person is capable ofinteracting with first modeling module 301 such as a keyboard, mouse, ortouch-screen display. Input that is entered through user interface 303can be stored in database 305. For example, models and parameters 307can include uploaded reservoir data and user defined parameters.Modeling software 309 cab use information stored in database 305 togenerate models representative of the reservoir. Models and parameters307 can also include models generated by modeling software 309. Duringoperation of first modeling module 301, processor 311 processesinstructions to execute modeling software 309. As will be described morefully herein, instructions from both user interface 303 and from othermodules, can be processed by processor 311 for operation of firstmodeling module 301. Processor 311 can process information that istransferred to and from first modeling module 301 through input/outputdevice 313. One skilled in the art will appreciate that, if desired, aplurality of processors in communication with first modeling module 301can be employed to reduce the computational time needed to generate amodel.

Similar to first modeling module 301, second modeling module 315 is usedto generate models representative of a reservoir, such as subterraneanreservoir 1. Second modeling module 315 includes a user interface 317 inwhich the operator can enter data, review ongoing operations, or controloperation of second modeling module 315. User interface 317 can be anymeans in which a person is capable of interacting with second modelingmodule 315 such as a keyboard, mouse, or touch-screen display. Inputthat is entered through user interface 317 can be stored in database319. For example, models and parameters 321 can include uploadedreservoir data and user defined parameters. Modeling software 323 canuse information stored in database 319 to generate models representativeof the reservoir. Models and parameters 321 can also include modelsgenerated by modeling software 323. During operation of second modelingmodule 315, processor 325 processes instructions to execute modelingsoftware 323. As will be described more fully herein, instructions fromboth user interface 317 and from other modules, can be processed byprocessor 325 for operation of second modeling module 315. Processor 325can process information that is transferred to and from second modelingmodule 315 through input/output device 327. One skilled in the art willappreciate that, if desired, a plurality of processors in communicationwith second modeling module 315 can be employed to reduce thecomputational time needed to generate a model.

Control management module 329 is used to control operation of system300. Control management module 329 includes a user interface 331 toinput information and review operations of system 300, includingoperations of control management module 329. User interface 331 can beany means in which a person is capable of interacting with controlmanagement module 329 such as a keyboard, mouse, or touch-screendisplay. Input that is entered through user interface 331 can be storedin database 333. For example, models and parameters 335 can includeuploaded reservoir data and user defined parameters. Additionally, insome embodiments the operator can input a performance objective 337.Control management module 329 also includes control software 341. Aswill be described more fully herein, control software 341 includes aplurality of modules including a model retriever 343, model generator345, proxy generator 347, model filter 349, and a model evaluator 351.During operation of control management module 329, processor 353processes instructions to execute control software 341. Processor 353can process information that is transferred to and from controlmanagement module 329 through input/output device 357. One skilled inthe art will appreciate that, if desired, a plurality of processors incommunication with control management module 329 can be employed.

In certain embodiments, system 300 can include reporting unit 359 toprovide information to the operator. For example, reporting unit 359 canbe a printer, display screen, or a data storage device. However, itshould be understood that system 300 need not include reporting unit359, and alternatively any one of user interfaces 303, 317, 331 can beutilized for reporting of information in system 300 to the operator.

Communication between any components of system 300, such as firstmodeling module 301, second modeling module 315, control managementmodule 329 and reporting unit 359, can be transferred overcommunications network 361. First modeling module 301 is connected tocommunications network 361 through input/output device 313, secondmodeling module 315 is connected to communications network 361 throughinput/output device 327, and control management module 329 is connectedto communications network 361 through input/output device 357.Communications network 361 can be any means that allows for informationtransfer. Examples of such a communications network 361 presentlyinclude, but are not limited to, a personal area network (PAN), a localarea network (LAN), a wide area network (WAN), and a global area network(GAN). Communications network 361 can also include any hardwaretechnology used to connect the individual devices in the network, suchas an optical cable or wireless radio frequency.

In operation of system 300, first and second models representing areservoir are generated using first modeling module 301 and secondmodeling module 315. First and second models, typically comprised of aplurality of parameters embodying characteristics of the reservoir, canhe stored respectively in databases 305, 319. One skilled in the artwill appreciate that first modeling module 301 and second modelingmodule 315 can be operated concurrently. The performance objective 337,which can be defined either by the operator or by the control software341, dictates the operation of system 300.

Operation of system 340 typically includes iterative refinement of thereservoir models. Iterative refinement of the reservoir models beginswith the model retriever 343 of control software 341 retrieving modelsand parameters stored in databases 305, 319 of first and second modelingmodules 301 and 315, respectively. Data received from the first andsecond modeling models can be stored in database 333 and used by modelgenerator 345 to generate offspring models using first modeling software301 and second modeling software 315. As offspring models are generated,proxy generator 347 can generate proxy functions 339 responsive to themodels. Typical types of proxy functions that are generated by proxygenerator 339 include kriging proxies, neural network proxies, splinesproxies, and polynomial proxies. Formulated proxy functions 339 arestored in database 333 for each generation of simulated models. Proxyfunctions 339 can be used by model filter 349 to prohibit modelgenerator 345 from generating offspring models if an estimate from theproxy functions 339 is reliable. Additionally, proxy functions 339 canbe used in subsequent operations to replace numerical simulators.Offspring models that are generated by first modeling module 301 orsecond modeling module 315 are evaluated by model evaluator 351 todetermine whether the offspring model satisfies the stored performanceobjective 337. If the offspring model does not satisfies the performanceobjective 337, iterative refinement of the reservoir models continues.If the offspring model satisfies the performance objective 337, aforecast can be generated for the hydrocarbon producing reservoir. Theforecast can be outputted to the operator with reporting unit 359 or canbe reported through any one of user interfaces 303, 317, or 331.

The above systems and methods integrate the workflows of computationalmodeling to efficiently forecast hydrocarbon production characteristicsfrom subterranean reservoirs. The workflows eliminate error due toinaccurate assessment and poor knowledge capture during transfer of dataacross the multiple disciplines needed. They streamline the process ofproduction data model refinement and history matching optimization.Therefore, the present invention enables more efficient forecasting ofhydrocarbon production, while mitigating model uncertainty. While thisinvention has been described in relation to certain preferredembodiments, and many details have been set forth for the purpose ofillustration, it will be apparent to those skilled in the art that theinvention is susceptible to alteration and that certain other detailsdescribed herein can vary considerably without departing from the basicprinciples of the invention.

What is claimed is:
 1. A computer-implemented method to automateforecasting of a hydrocarbon producing reservoir, the method comprising:(a) providing a performance objective associated with forecasting of ahydrocarbon producing reservoir via a user interface in communicationwith a reservoir modeling program, wherein the performance objective isan uncertainty assessment, and the uncertainty assessment comprises anexperimental design table including a parameter selected from the groupconsisting of permeability distributions, porosity distributions, fluidcontacts, fluid saturations, geobody connectivity, pore volume, faulttransmissibility, sedimentological properties, reservoir fluid flowrates, reservoir pressure characteristics, reservoir temperaturecharacteristics, upscaling properties, and training images; (b)automatically generating a gridded static model representative of atleast a portion of the hydrocarbon producing reservoir via a staticmodeling module of the reservoir modeling program using static reservoirdata and a discrete set of static parameters; (c) automaticallygenerating a dynamic model representative of at least a portion of thehydrocarbon producing reservoir via a dynamic modeling module of thereservoir modeling program using the static model and a discrete set ofdynamic parameters; (d) evaluating the dynamic model against theperformance objective via a control management module of the reservoirmodeling program; (e) until the performance objective is satisfied,iteratively combining the static and dynamic models, via the controlmanagement module of the reservoir modeling program, to automaticallygenerate an offspring model per iteration, wherein combining the staticand dynamic models includes regenerating at least one of the dynamicmodel or the static model during each iteration; and (f) outputting aresult according to the performance objective associated with theforecasting of the hydrocarbon producing reservoir subsequent tocompletion of step (e).
 2. The method of claim 1, wherein both thestatic and dynamic modeling modules can operate concurrently.
 3. Themethod of claim 1, wherein step (f) is performed via the controlmanagement module.
 4. The method of claim 1, wherein: step (e) furthercomprises generating a proxy function responsive to the offspring model.5. The method of claim 1, further comprising performing an optimizationstudy; the result according to the optimization study comprises anobjective function associated with the hydrocarbon producing reservoir;and the objective function includes at least one of maximizing andminimizing an item selected from the group consisting of ultimatehydrocarbon recovery, net present value, reservoir percentage yield,reservoir fluid flow rate, and history matching error.
 6. The method ofclaim 5, wherein the optimization study is satisfied once one of thefollowing steps occur selected from the group consisting of theoffspring model has been generated a predetermined number of iterations,and the offspring model converges to a predetermined variance.
 7. Themethod of claim 1, wherein step (e) of claim 1 further comprisesgenerating the offspring model responsive to a previously generatedoffspring model.
 8. The method of claim 1, wherein: the result accordingto the uncertainty assessment is a sensitivity of the parameter; and theperformance objective is satisfied once the sensitivity of the parameterhas been determined.
 9. The method of claim 1, wherein the dynamic modeldefines a structured or unstructured grid automatically generated fromthe static model at a coarse resolution for reservoir simulation.
 10. Acomputer-implemented method to automate forecasting of a hydrocarbonproducing reservoir, the method comprising: (a) modeling a griddedstatic model representative of at least a portion of a hydrocarbonproducing reservoir with a static modeling module, wherein the griddedstatic model is automatically generated using static reservoir data anda discrete set of static parameters; (b) modeling a dynamic modelrepresentative of at least a portion of the hydrocarbon producingreservoir with a dynamic modeling module, wherein the dynamic model isautomatically generated using the static model and a discrete set ofdynamic parameters; (c) generating a discrete set of parametersresponsive to the static model and the dynamic model with a controlmanagement module, wherein the discrete set of parameters is selectedfrom the group consisting of permeability distributions porositydistributions, fluid contacts, fluid saturations, geobody connectivity,pore volume, fault transmissibility, sedimentological properties,reservoir fluid flow rates, reservoir pressure characteristics,reservoir temperature characteristics, upscaling properties, andtraining images; (d) modeling an offspring model responsive to at leastone parameter from the discrete set of parameters, the modeling of theoffspring model is performed with at least one of the static and dynamicmodeling modules, and wherein the modeling of the offspring modelincludes combining the static and dynamic models to automaticallygenerate the offspring model; (e) updating the discrete set ofparameters responsive to the offspring model with the control managementmodule; (f) repeating steps (d) and (e), including regenerating at leastone of the dynamic model or the static model during each iteration,until one of the following occurs selected from the group consisting ofthe offspring model has been generated a predetermined number ofiterations, the offspring model converges to a predetermined variance,and a sensitivity of the at least one parameter has been determined; and(g) outputting a visual display of a solution associated withforecasting of the hydrocarbon producing reservoir subsequent tocompletion of step (f), wherein the visual display of the solutioncomprises a result to an uncertainty assessment.
 11. The method of claim10, wherein the offspring model is a static model when the modeling ofthe offspring model is generated with the static modeling module and theoffspring model is a dynamic model when the modeling of the offspringmodel is generated with the dynamic modeling module.
 12. The method ofclaim 10, wherein the visual display of the solution comprises a resultof an optimization study.
 13. A system to automate forecasting of ahydrocarbon producing reservoir, the system comprising: a computerprocessor; a computer program executable on the computer processor, thecomputer program comprising: a first modeling module to automaticallygenerate a first model representative of at least a portion of thehydrocarbon producing reservoir using first reservoir data and a firstdiscrete set of parameters, wherein the first modeling module is astatic modeling model and the first model is a static model; a secondmodeling module to generate a second model representative of at least aportion of the hydrocarbon producing reservoir using the first model anda second discrete set of parameters, wherein the second modeling moduleis a dynamic modeling model and the second model is a dynamic model; anda control management module to evaluate the second model against aperformance objective associated with the forecasting of the system, anduntil the performance objective is satisfied, to automatically generateoffspring models responsive to the first and second models with at leastone of the first modeling module and the second modeling module, whereingenerating the offspring models includes regenerating at least one ofthe second model or the first model, and wherein the control managementmodule outputs a result according to the performance objectivesubsequent to the performance objective being satisfied, wherein theperformance objective is an uncertainty assessment, and the uncertaintyassessment comprises an experimental design table including a parameterselected from the group consisting of permeability distributions,porosity distributions, fluid contacts, fluid saturations, geobodyconnectivity, pore volume, fault transmissibility, sedimentologicalproperties, reservoir fluid flow rates, reservoir pressurecharacteristics, reservoir temperature characteristics, upscalingproperties, and training images.
 14. The system of claim 13, furthercomprising an optimization study; the result to the optimization studycomprising an objective function associated with the hydrocarbonproducing reservoir; the objective function includes at least one ofmaximizing and minimizing an item selected from the group consisting ofultimate hydrocarbon recovery, net present value, reservoir percentageyield, reservoir fluid flow rate, and history matching error; and theoptimization study is satisfied once one of the following occursselected from the group consisting of the offspring model has beengenerated a predetermined number of iterations, and the offspring modelconverges to a predetermined variance.
 15. The system of claim 13,wherein the result to the uncertainty assessment is a sensitivity of theparameter; and the performance objective is satisfied once thesensitivity of the parameter has been determined.
 16. A system toautomate forecasting of a hydrocarbon producing reservoir, the systemcomprising: a computer processor; a software program executable on thecomputer processor, the software program comprising: (a) a firstmodeling module to automatically generate a first model representativeof at least a portion of the hydrocarbon producing reservoir using firstreservoir data and a first discrete set of parameters, wherein the firstmodeling module is a static modeling module and the first model is astatic model; (b) a second modeling module to automatically generate asecond model representative of at least a portion of the hydrocarbonproducing reservoir using the first model and a second discrete set ofparameters, wherein the second modeling module is a dynamic modelingmodule and the second model is a dynamic model; and (c) a controlmanagement module that initiates a plurality of operations for thesoftware program responsive to a performance objective, the controlmanagement module including at least one of a model retriever toretrieve the first and second models over a communications network, amodel generator to automatically generate offspring models responsive tothe first and second models with the first and second modeling modules,wherein generating the offspring models includes regenerating at leastone of the second model or the first model, a proxy generator togenerate a proxy function responsive to the first and second models, amodel filter that prohibits the model generator from generating theoffspring models responsive to the proxy function, and a model evaluatorto evaluate the offspring models and generate a forecast for thehydrocarbon producing reservoir, (d) wherein the performance objectiveis an uncertainty assessment, and the uncertainty assessment comprisesan experimental design table including a parameter selected from thegroup consisting of permeability distributions, porosity distributions,fluid contacts, fluid saturations, geobody connectivity, pore volume,fault transmissibility, sedimentological properties, reservoir fluidflow rates, reservoir pressure characteristics, reservoir temperaturecharacteristics, upscaling properties, and training images; a usercontrol interface for inputting information into the system, theinformation including the performance objective; a database to store theperformance objective, first and second models, and offspring models;and a user reporting means in communication with the control managementmodule over the communications network to output the forecast for thehydrocarbon producing reservoir.
 17. The system of claim 16, furthercomprising performing an optimization study; the optimization studycomprising an objective function associated with the hydrocarbonproducing reservoir; the objective function includes at least one ofmaximizing and minimizing an item selected from the group consisting ofultimate hydrocarbon recovery, net present value, reservoir percentageyield, reservoir fluid flow rate, and history matching error.
 18. Anon-transitory processor readable medium containing computer readablesoftware instructions used to automate forecasting of a hydrocarbonproducing reservoir, the software comprising: (a) a first modelingmodule to automatically generate a first model representative of atleast a portion of the hydrocarbon producing reservoir using firstreservoir data and a first discrete set of parameters, wherein the firstmodeling module is a static modeling module and the first model is astatic model; (b) a second modeling module to automatically generate asecond model representative of at least a portion of the hydrocarbonproducing reservoir using the first model and a second discrete set ofparameters, wherein the second modeling module is a dynamic modelingmodule and the second model is a dynamic model; and (c) a controlmanagement module that initiates a plurality of operations for thesoftware program responsive to a performance objective, the controlmanagement module including at least one of a model retriever toretrieve the first and second models over a communications network, amodel generator to automatically generate offspring models responsive tothe first and second models with the first and second modeling modules,wherein generating the offspring models includes regenerating at leastone of the second model or the first model, a proxy generator togenerate a proxy function responsive to the first and second models, amodel filter that prohibits the model generator from generating theoffspring models responsive to the proxy function, and a model evaluatorto evaluate the offspring models and generate a forecast for thehydrocarbon producing reservoir, (d) wherein the performance objectiveis an uncertainty assessment, and the uncertainty assessment comprisesan experimental design table including a parameter selected from thegroup consisting of permeability distributions, porosity distributions,fluid contacts, fluid saturations, geobody connectivity, pore volume,fault transmissibility, sedimentological properties, reservoir fluidflow rates, reservoir pressure characteristics, reservoir temperaturecharacteristics, upscaling properties, and training images.
 19. Thenon-transitory processor readable medium of claim 18, wherein the proxyfunction is selected from the group consisting of kriging, neuralnetwork, splines, and polynomial proxy functions.